Update sam_module.py

sam_module.py

@@ -808,10 +808,14 @@ def create_sam_mask(self, image, bbox_coords, mode):
                     print(f"       • Kompaktheits-Score: {compactness_score:.3f}")
     
                 # 3. BBOX-ÜBERLAPPUNG (20%)
-                bbox_mask = np.zeros((image.height, image.width), dtype=np.uint8)
-                bbox_mask[y1:y2, x1:x2] = 1
-                overlap = np.sum(mask_binary & bbox_mask)
+                bbox_mask = np.zeros((original_image.height, original_image.width), dtype=np.uint8)
+                bbox_mask[original_bbox[1]:original_bbox[3], original_bbox[0]:original_bbox[2]] = 1
+                         overlap = np.sum(mask_binary & bbox_mask)
                 bbox_overlap_ratio = overlap / mask_area_pixels if mask_area_pixels > 0 else 0
+        
+               print(f"   🔍 Maske {i+1}: Fläche={mask_area_pixels:,}px, "
+                     f"Überlappung={overlap:,}px ({bbox_overlap_ratio:.1%})") 
+              
     
                 # Für Kopf: Sollte großteils in BBox sein (mind. 70%)
                 if bbox_overlap_ratio >= 0.7:
@@ -850,7 +854,7 @@ def create_sam_mask(self, image, bbox_coords, mode):
             print(f"✅ Beste Maske ausgewählt: Nr. {best_mask_idx+1} mit Score {best_score:.3f}")
 
             # Beste Maske verwenden
-            mask_np = all_masks[best_mask_idx]
+            mask_np = all_masks_crop[best_mask_idx]
             max_val = mask_np.max()
             print(f"🔍 Maximaler SAM-Konfidenzwert der besten Maske: {max_val:.3f}")